scholarly journals Forecasting Passenger Flow Distribution on Holidays for Urban Rail Transit Based on Destination Choice Behavior Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Enjian Yao ◽  
Junyi Hong ◽  
Long Pan ◽  
Binbin Li ◽  
Yang Yang ◽  
...  
Keyword(s):  
Public Transportation ◽  
Choice Model ◽  
Flow Distribution ◽  
Urban Rail Transit ◽  
Destination Choice ◽  
Rail Transit ◽  
Efficient Operation ◽  
Passenger Flow ◽  
Urban Rail ◽  
Proposed Model

Passenger travel flows of urban rail transit during holidays usually show distinct characteristics different from normal days. To ensure efficient operation management, it is essential to accurately predict the distribution of holiday passenger flow. Based on Automatic Fare Collection (AFC) data, this paper explores the passengers’ destination choice differences between normal days and holidays, as well as one-way tickets and public transportation cards, which provides support for variable selection in modeling. Then, a forecasting model of holiday travel distribution is proposed, in which the destination choice model is established for representing local and nonlocal passengers. Meanwhile, explanatory variables such as land matching degree, scenic spot dummy, and level of service variables are introduced to deal with the particularity of holiday passengers’ travel behavior. The parameters calibrated by the improved weighted exogenous sampling maximum likelihood (WESML) method are applied to predict passenger flow distribution in different holiday cases with annual changes in the metro network, using the data collected from Guangzhou Metro, China. The results show that the proposed model is valid and performs better than the other comparable models in terms of forecasting accuracy. The proposed model has the capability to provide a more universal and accurate passenger flow distribution prediction method for urban rail transit in different holiday scenarios with network changes.

scholarly journals Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Chang-jun Cai ◽  
En-jian Yao ◽  
Sha-sha Liu ◽  
Yong-sheng Zhang ◽  
Jun Liu
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Travel Demand ◽  
Choice Model ◽  
Urban Rail Transit ◽  
Destination Choice ◽  
Rail Transit ◽  
Transit System ◽  
Passenger Flow ◽  
Urban Rail

For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengers’ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Year’s day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Year’s days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that theρ2values are improved by 0.060, 0.045, and 0.040 for January 1, January 2, and January 3, respectively, with the consideration of destination attractiveness.

Influence of Emergency Passenger Flow Distribution in Urban Rail Network

2012 ◽  
Vol 450-451 ◽  
pp. 295-301 ◽  
Author(s):  
Ling Hong ◽  
Jia Gao ◽  
Rui Hua Xu
Keyword(s):  
Flow Distribution ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Abstract Model ◽  
Flow Volume ◽  
Rail Network ◽  
Passenger Flow ◽  
Urban Rail ◽  
Passenger Flows ◽  
Urban Rail Transit Network

The emergency disposal of urban rail transit needs to accurately estimate the emergency range and total affected passenger flow volume. The urban rail transit network could be simplified to an abstract model which is easy to be analyst based on the graph theory method. Considering the actual network back-turning lines and vehicle storage tracks of urban rail network, the emergency range could be estimated effectively. The affected passenger flow could be classified as different kinds based on the different paths of passenger flow. The classification of passenger flow mainly includes “delay passenger flow”, “detour passenger flow” and “loss passenger flow”. Considering the emergency range, the different affected passenger flows could be superposed over time based on the abstract model, then the affected passenger flow volume and virtual loss time could be calculated out. The results could provide basis for the emergency disposal in urban rail transit. The example analysis is verified the feasibility of this method.

scholarly journals Modeling and Simulation of Passenger Flow Distribution in Urban Rail Transit Hub Platform

2016 ◽  
Author(s):  
Long Gao ◽  
Limin Jia
Keyword(s):  
Modeling And Simulation ◽  
Superposition Principle ◽  
Flow Distribution ◽  
Service Level ◽  
Urban Rail Transit ◽  
Distribution Model ◽  
Rail Transit ◽  
Simulation Design ◽  
Passenger Flow ◽  
Urban Rail

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

scholarly journals A Passenger Flow Control Method for Subway Network Based on Network Controllability

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Lu Zeng ◽  
Jun Liu ◽  
Yong Qin ◽  
Li Wang ◽  
Jie Yang
Keyword(s):  
Flow Control ◽  
Control Method ◽  
Flow Distribution ◽  
Urban Rail Transit ◽  
Flow Density ◽  
Rail Transit ◽  
System Control ◽  
Flow Network ◽  
Passenger Flow ◽  
Urban Rail

The volume of passenger flow in urban rail transit network operation continues to increase. Effective measures of passenger flow control can greatly alleviate the pressure of transportation and ensure the safe operation of urban rail transit systems. The controllability of an urban rail transit passenger flow network determines the equilibrium state of passenger flow density in time and space. First, a passenger flow network model of urban rail transit and an evaluation index of the alternative set of flow control stations are proposed. Then, the controllable determination model of the urban rail transit passenger flow network is formed by converting the passenger flow distribution into a system state equation based on system control theory. The optimization method of passenger flow control stations is established via driver node matching to realize the optimized control of network stations. Finally, a real-world case study of the Beijing subway network is presented to demonstrate that the passenger flow network is controllable when driver nodes compose 25.3% of the entire network. The optimization of the flow control station, set during the morning peak, proves the efficiency and validity of the proposed model and algorithm.

scholarly journals Modeling and Simulation of Passenger Flow Distribution in Urban Rail Transit Hub Platform

2016 ◽  
Author(s):  
Long Gao ◽  
Limin Jia
Keyword(s):  
Traffic Safety ◽  
Flow Distribution ◽  
Simulation Models ◽  
Performance Comparison ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Main Mode ◽  
Dynamic Variation ◽  
Passenger Flow ◽  
Urban Rail

Urban rail transit has become the main mode to ease traffic congestion. Researches on the dynamic variation regularity of passenger flow distribution and passenger flow volume of urban rail transit hub platform have important implication in hub capacity design, operation organization and risk prevention. This paper proposes a passenger flow distribution integrating model (PFDIM) based on the basic theory analysis and basic parameter models of passenger flow distribution in hub platform. Simulation designs of PFDIM are built with Java and Anylogic, which contain simulation system functional framework, implementation path, simulation processes and simulation models. By case study, the performance comparison between two simulation methods indicates that simulation designs are scientific and accordant with the reality scene; calculation results prove that PFDIM has a good performance on describing the dynamic variation regularity of passenger flow distribution. Moreover, the experiments with different departure intervals could provide the reference for train scheduling under the viewpoint of traffic safety and service level.

scholarly journals Sustainable Development of Urban Rail Transit Networks: A Vulnerability Perspective

2019 ◽  
Vol 11 (5) ◽  
pp. 1335 ◽  
Author(s):  
Jiangang Shi ◽  
Shiping Wen ◽  
Xianbo Zhao ◽  
Guangdong Wu
Keyword(s):  
Sustainable Development ◽  
Public Transportation ◽  
Fault Tolerant ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
The Sustainable Development ◽  
Transportation Services ◽  
Passenger Flow ◽  
Urban Rail ◽  
Key Nodes

Urban rail transit (URT) systems are critical to modern public transportation services. Unfortunately, disruptions in URT systems can lead to dysfunction and threaten sustainable development. This study analyses URT network sustainability from a vulnerability perspective. Two network attack scenarios, including random attacks and intentional attacks, are designed to assess different kinds of disruptions to URT networks. Under random attacks, nodes are randomly removed from the network. In contrast, under intentional attacks, key nodes are identified and removed based on topological metrics and passenger flow volume. Then, URT network vulnerability is evaluated by quantifying the changes in network efficiency and structural integrity under the network attacks from a spatio-temporal point of view. The real-world case of the Shanghai URT system from 1993 to 2020 is used to illustrate the vulnerability in the evolution of the URT system. The results indicate that the URT network is increasingly fault-tolerant and structurally robust over time. The URT network is more vulnerable to intentional attacks than to random failures. Additionally, there are significant spatial differences in the vulnerability of Shanghai URT network. Stations in the central activity zone (CAZ) are more fault-tolerant and robust than stations located outside of the CAZ. Furthermore, stations with large centrality and greater passenger flow volumes and lines with many key nodes and greater passenger flow volumes, are vulnerable to disruptions in the URT networks. This study provides a new index to comprehensively quantify node centrality; it also fills a research gap by analysing the vulnerability of URT networks based on both longitudinal and spatial patterns. Finally, this paper highlights significant practical implications for the sustainable development of URT networks, as well as the sustainable development of public transportation services.

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